Rotor trajectory index for transient security assessment using Radial Basis Function Neural Network

With the present trend towards deregulation of electricity, there is increasing need to ensure transient security for existing or forecasted operating conditions of networks at the Energy Management Systems. In this paper, rotor trajectory index (RTI) is proposed for identifying the transient security status of each generator in terms of their synchronism. Radial Basis Function Neural Network (RBFNN) based method is proposed for Transient Security Assessment (TSA) of power systems. Two different feature selection methods have also been investigated to select the appropriate number of features for neural network training. The effectiveness of the proposed methodology is demonstrated on IEEE 145-bus, 50-generator system at various loading conditions corresponding to single and multiple line outages. The application result shows excellent classification accuracy on unseen load samples and therefore the proposed method may serve as promising tool for online TSA.